The pathology of many cardiovascular diseases is believed to be heavily tied to local hemodynamics. A positive feedback loop of remodeling and unhealthy flow conditions can lead to blood vessel abnormalities, such as aneurysms, stenoses, and plaques. Doctors use medical imaging techniques to visualize vascular disease within patients. However, while treatments based on these images for some diseases have been well-established, there is room for improvement. Many treatment decisions are based on simplified criteria and incomplete data and our understanding behind the pathophysiology of many of these diseases remains woefully incomplete. A stronger grasp of the relationship between disease and hemodynamics can help clinicians plan better treatment outcomes or help prevent some complications from occurring.
4D-Magnetic Resonance (MR) Imaging can be a powerful tool for quantifying the flow field within the cardiovasculature, but remains difficult to integrate for clinical use due to the involving process required to convert noisy magnetic resonance image data from a set of symbols to an easily digestible image. Treatment decisions are based on standardized practices, but no standardization for the processing and interpretation of 4D-MR images exist. Computational Fluid Dynamics (CFD) can simulate patient hemodynamics with much finer temporal and spatial resolution than 4D-MR. However, computational methods must still use boundary conditions obtained from imaging data, which means CFD results can only be as accurate as the images they are based upon. Additionally, the logistics of performing CFD, which require extensive training and time, is a strong barrier against its adoption in a clinical setting.
This thesis presents a model for using in vivo (live) and simulated flow data as complementary methods to explore potential mechanisms of pathophysiology and aid the treatment of cardiovascular diseases. We developed pipelines for processing 4D-MR images and for building patient-specific CFD simulations. We first attempted to measure the experimental error in 4D-Flow MRI within a cerebral aneurysm phantom and explored how that error might propagate into CFD results based on those MR measurements. We used those results to predict how this sensitivity would affect the treatment of aneurysms based on their hemodynamics. We then used both 4D-MR and CFD methods to characterize a rarely explored vascular territory, the cerebral venous outflow tract (CVOT). 4D-Flow MR was used to categorize geometries and flows in the CVOT. CFD was used to investigate a potential association between flow and symptoms in a subcategory of internal jugular vein geometries.
